Patients with ER-negative breast cancer have the worst prognosis of all breast cancer subtypes, often experiencing rapid recurrence or progression to metastatic disease shortly after diagnosis. Given that metastasis is the primary cause of mortality in most solid tumors, understanding metastatic biology is crucial for effective intervention. Using a mouse systems genetics approach, we previously identified 12 genes associated with metastatic susceptibility. Here, we extend those studies to identify Resf1, a poorly characterized gene, as a novel metastasis susceptibility gene in ER- breast cancer. Resf1 is a large, unstructured protein with an evolutionarily conserved intron-exon structure, but with poor amino acid conservation. CRISPR or gene trap mouse models crossed to the Polyoma Middle-T antigen genetically engineered mouse model (MMTV-PyMT) demonstrated that reduction of Resf1 resulted in a significant increase in tumor growth, a shortened overall survival time, and increased incidence and number of lung metastases, consistent with patient data. Furthermore, an analysis of matched tail and primary tissues revealed loss of the wildtype copy in tumor tissue, consistent with Resf1 being a tumor suppressor. Mechanistic analysis revealed a potential role of Resf1 in transcriptional control through association with compound G4 quadruplexes in expressed sequences, particularly those associated with ribosomal biogenesis. These results suggest that loss of Resf1 enhances tumor progression in ER- breast cancer through multiple alterations in both transcriptional and translational control.
Triple Negative Breast Neoplasms , Triple Negative Breast Neoplasms/genetics , Triple Negative Breast Neoplasms/pathology , Animals , Mice , Female , Humans , G-Quadruplexes , Genes, Tumor Suppressor , Gene Expression Regulation, Neoplastic , Cell Line, Tumor , Lung Neoplasms/genetics , Lung Neoplasms/secondary , Lung Neoplasms/pathology , Neoplasm Metastasis
BACKGROUND: Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. MATERIALS AND METHODS: This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol's resilience against attacks. RESULTS: The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol's communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol's security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. CONCLUSIONS: As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose.
High power is a critical requirement of lithium-ion batteries designed to satisfy the load profiles of advanced air mobility. Here, we simulate the initial takeoff step of electric vertical takeoff and landing (eVTOL) vehicles powered by a lithium-ion battery that is subjected to an intense 15C discharge pulse at the beginning of the discharge cycle followed by a subsequent low-rate discharge. We conducted extensive electrochemical testing to assess the long-term stability of a lithium-ion battery under these high-strain conditions. The main finding is that despite the performance recovery observed at low rates, the reapplication of high rates leads to drastic cell failure. While the results highlight the eVTOL battery longevity challenge, the findings also emphasize the need for tailored battery chemistry designs for eVTOL applications to address both anode plating and cathode instability. In addition, innovative second-use strategies would be paramount upon completion of the eVTOL services.
Chalcogel represents a unique class of meso- to macroporous nanomaterials that offer applications in energy and environmental pursuits. Here, the synthesis of an ion-exchangeable amorphous chalcogel using a nominal composition of K2 CoMo2 S10 (KCMS) at room temperature is reported. Synchrotron X-ray pair distribution function (PDF), X-ray absorption near-edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) reveal a plausible local structure of KCMS gel consisting of Mo5+ 2 and Mo4+ 3 clusters in the vicinity of di/polysulfides which are covalently linked by Co2+ ions. The ionically bound K+ ions remain in the percolating pores of the Co-Mo-S covalent network. XANES of Co K-edge shows multiple electronic transitions, including quadrupole (1sâ3d), shakedown (1sâ4p + MLCT), and dipole allowed 1sâ4p transitions. Remarkably, despite a lack of regular channels as in some crystalline solids, the amorphous KCMS gel shows ion-exchange properties with UO2 2+ ions. Additionally, it also presents surface sorption via [SââââUO2 2+ ] covalent interactions. Overall, this study underscores the synthesis of quaternary chalcogels incorporating alkali metals and their potential to advance separation science for cations and oxo-cationic species by integrating a synergy of surface sorption and ion-exchange.
Background and Aims: Skin aging is associated with dry skin and a decrease of the strength of the dermoepidermal adhesion, which increases the risk for lacerations (skin tears). Application of leave-on products improves dry skin and seems to reduce skin tear incidence. The aim of this study was to measure the effects of a humectant containing leave-on product on the strength of the dermoepidermal junction in older adult participants with dry skin. Methods: A randomized controlled trial using a split body design was conducted. One forearm was randomly selected and treated with a lipophilic leave-on product containing 5% urea for 8 weeks. The other forearm was the control. The parameters stratum corneum hydration (SCH), transepidermal water loss, pH, roughness, epidermal thickness and skin stiffness were measured at the baseline, Weeks 4 and 8. At Week 8, suction blisters were created and time to blistering was measured. Blister roofs and interstitial fluid were analyzed for Interleukin-1α, 6 and 8. Results: Twelve participants were included. After 8 weeks treatment, SCH was higher (median difference 11.6 AU), and the overall dry skin score (median difference -1) and median roughness (Rz difference -12.2 µm) were lower compared to the control arms. The median group difference for Interleukin-1α was -452 fg/µg total protein (TP) in the blister roofs and -2.2 fg/µg TP in the blister fluids. The median time to blister formation was 7.7 min higher compared to the control arms. Conclusion: The regular application of humectant containing leave-on products improves dry skin and seems to lower inflammation and contribute to the strengthening of the dermoepidermal adhesion. This partly explains how the use of topical leave-on products helps to prevent skin tears.
Despite large theoretical energy densities, metal-sulfide electrodes for energy storage systems face several limitations that impact the practical realization. Here, we present the solution-processable, room temperature (RT) synthesis, local structures, and application of a sulfur-rich Mo3S13 chalcogel as a conversion-based electrode for lithium-sulfide batteries (LiSBs). The structure of the amorphous Mo3S13 chalcogel is derived through operando Raman spectroscopy, synchrotron X-ray pair distribution function (PDF), X-ray absorption near edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) analysis, along with ab initio molecular dynamics (AIMD) simulations. A key feature of the three-dimensional (3D) network is the connection of Mo3S13 units through S-S bonds. Li/Mo3S13 half-cells deliver initial capacity of 1013â mAh g-1 during the first discharge. After the activation cycles, the capacity stabilizes and maintains 312â mAh g-1 at a C/3 rate after 140â cycles, demonstrating sustained performance over subsequent cycling. Such high-capacity and stability are attributed to the high density of (poly)sulfide bonds and the stable Mo-S coordination in Mo3S13 chalcogel. These findings showcase the potential of Mo3S13 chalcogels as metal-sulfide electrode materials for LiSBs.
Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %-12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.
The indicators of economic and sustainable development ultimately significantly depend on carbon dioxide (CO2) emissions in every country. In Bangladesh, there is an increasing trend in population, industrialization, as well as electricity demand generated from different sources, ultimately increasing CO2 emissions. This study explores the relationship between CO2 emissions and other significant relevant indicators. Moreover, the authors aimed to identify which model is effective at predicting CO2 emissions and assess the accuracy of the prediction of different models. The secondary data from 1971 to 2020, was collected from the World Bank and the Bangladesh Road Transport Authority's publicly accessible website. The generalized additive model (GAM), the polynomial regression (PR), and multiple linear regression (MLR) were used for modeling CO2 emissions. The model performance is evaluated using the Bayesian information criterion (BIC), Akaike information criterion (AIC), Root mean square error (RMSE), R-square, and mean square error (MSE). Results revealed that there are few multicollinearity problems in the datasets and exhibit a nonlinear relationship among CO2 emissions. Among the models considered in this study, the GAM model has the lowest value of RMSE = 0.008, MSE = 0.000063, AIC = -303.21, BIC = -266.64 and the highest value of R-squared = 0.996 compared to the MLR and PR models, suggesting the most appropriate model in predicting CO2 emissions in Bangladesh. Findings revealed that the total CO2 emissions and other relevant risk factors is non-linear. The study suggests that the Generalized additive model regression technique can be used as an effective tool for predicting CO2 emissions in Bangladesh. The authors believed that the findings would be helpful to policymakers in designing effective strategies in the areas of a low-carbon economy, encouraging the use of renewable energy sources, and focusing on technological advancement that reduces CO2 emissions and ensures a sustainable environment in Bangladesh.
Acetylation of protein and RNA represent a critical event for development and cancer progression. NAT10 is the only known RNA acetylase that catalyzes the N4-actylcytidine (ac4C) modification of RNAs. Here, we show that the loss of NAT10 significantly decreases lung metastasis in allograft and genetically engineered mouse models of breast cancer. NAT10 interacts with a mechanosensitive, metastasis susceptibility protein complex at the nuclear pore. In addition to its canonical role in RNA acetylation, we find that NAT10 interacts with p300 at gene enhancers. NAT10 loss is associated with p300 mislocalization into heterochromatin regions. NAT10 depletion disrupts enhancer organization, leading to alteration of gene transcription necessary for metastatic progression, including reduced myeloid cell-recruiting chemokines that results in a less metastasis-prone tumor microenvironment. Our study uncovers a distinct role of NAT10 in enhancer organization of metastatic tumor cells and suggests its involvement in the tumor-immune crosstalk dictating metastatic outcomes.
AIM: The aim of the study was to describe types and frequencies of skin care interventions and products provided in institutional long-term care. MATERIALS AND METHODS: Baseline data from a cluster randomized controlled trial conducted in nursing homes in Berlin, Germany was collected before randomization. Numbers, proportions and frequencies of washing, showering and bathing, and the application of leave-on products were calculated. Product labels were iteratively and inductively categorized into overarching terms and concepts. RESULTS: A total of n = 314 residents participated in the study. In the majority, washing of the whole body was done once daily, and showering was performed once per week or more rarely. The majority received leave-on products daily on the face and once per week on the whole body. Most of the skin care interventions were delivered by nurses. There was marked heterogeneity in terms of product names, whereas the product names reveal little about the ingredients or composition. CONCLUSION: Personal hygiene and cleansing interventions are major parts of clinical practice in long-term care. Daily washing is a standard practice at the moment. In contrast, leave-on products are used infrequently. To what extent the provided care promotes skin integrity is unclear. Due to the heterogeneity and partly misleading labels of skin care products, informed decision making is difficult to implement at present. GOV IDENTIFIER: NCT03824886.
Long-Term Care , Skin Care , Humans , Cross-Sectional Studies , Skin Care/methods , Skin Care/standards , Skin Care/statistics & numerical data , Female , Long-Term Care/methods , Long-Term Care/standards , Long-Term Care/statistics & numerical data , Male , Germany , Aged, 80 and over , Aged , Nursing Homes/statistics & numerical data , Nursing Homes/standards , Nursing Homes/organization & administration
This manuscript introduces a highly sensitive dual-core photonic crystal fiber (PCF) based multi-analyte surface plasmon resonance (SPR) sensor, possessing the ability to detect multiple analytes at once. A chemically stable thin plasmonic substance of gold (Au) layer, holding a thickness of 30 nm, is employed to the outer portion of the stated design that manifests a negative real permittivity. Moreover, an ultra-thin film of aluminum oxide (Al2O3) , having a thickness of 10 nm, is inserted into the exterior of the gold film to calibrate the resonance wavelength as well as magnify the coupling strength. The performance of the sensor is rigorously explored employing the finite element method (FEM), where numerical investigation confirms that the intended sensor model exhibits a peak amplitude sensitivity (AS) of 2606 RIU-1 , as well as a highest wavelength sensitivity (WS) of 20,000 nm/RIU. The achieved outcomes affirm that the sensor design can be conceivably applied in numerous biological; as well as biochemical analyte refractive index (RI) detection to realize the relevant significant applications in the visible to near-infrared (VNIR) region of 0.5 to [Formula: see text].
Aluminum Oxide , Surface Plasmon Resonance , Gold , Vibration
Background: Caesarean section (C-section) in Bangladesh have received great attention as the number has been amplified during the last two decades. The question arises whether this rise has a correlation with other maternal healthcare services and/or has been influenced by their predictors. Objective: The main objectives of this study are to assess correlations among the maternal healthcare indicators-antenatal care use, childbirth in private facilities, and childbirth through C-section-and identify their associated predictors in Bangladesh through the development of an appropriate cluster-adjusted joint model that accounts for inter-correlation among the indicators in the same cluster. Design: The 2019 Bangladesh Multiple Indicator Cluster Survey data have been utilized in this study. Separate generalized linear mixed models developed for the three outcome variables are combined into a joint model by letting cluster-specific random effects be in association. Findings: The joint model shows that the number of antenatal cares is fairly positively correlated with delivery in private facilities and C-section, while the latter two are strongly positively correlated. Household socio-economic condition, women and their partners' education, women's exposure to mass media, place of residence, religion, and regional settings have significant influence on the joint likelihood of receiving antenatal care, choosing a private health facility for birth, and opting for C-section birth. Key conclusions and implications: The rising rate of C-section delivery over time is alarming for Bangladesh to achieve the World Health Organization target of 10-15 %. The joint model reveals that the rising rate of C-sections may be correlated with the choice of a private health facility as the delivery place. The study findings also suggest that maternal childbirth care is private-dominant and predominantly utilized by urban women with better education and higher socio-economic status. The policy should focus on strengthening the public health sector while also keeping importance in increasing coverage of maternal care services among the less well-off.
OBJECTIVES: To identify possible factors associated with different severities of xerosis cutis and to describe possible associations between (skin) care dependency and application of moisturizers. DESIGN: Cross-sectional study using baseline data from a cluster-randomized controlled trial. Demographic and health characteristics, skin physiological measurements, functional abilities and application of moisturizers were compared between the participants with mild and severe dry skin. Frequency of moisturization were also compared based on the participants' skin care dependency. RESULTS: The more distal the body area, the more severe xerosis were observed. There were no or minor differences between the groups, except for the stratum corneum hydration and skin surface pH. Participants with severe xerosis received moisturizers less often. Skin care dependent residents received moisturizers frequently. CONCLUSION: There is under-application regarding xerosis cutis treatment in long-term care. Skin care provided by nurses, in adequate frequencies, might be helpful compared to skin care performed by the residents themselves.
Long-Term Care , Skin Care , Aged , Humans , Activities of Daily Living , Cross-Sectional Studies , Prevalence , Randomized Controlled Trials as Topic
IFNγ, a type II interferon secreted by immune cells, augments tissue responses to injury following pathogenic infections leading to lethal acute lung injury (ALI). Alveolar macrophages (AM) abundantly express Toll-like receptor-4 and represent the primary cell type of the innate immune system in the lungs. A fundamental question remains whether AM generation of IFNg leads to uncontrolled innate response and perpetuated lung injury. LPS induced a sustained increase in IFNg levels and unresolvable inflammatory lung injury in the mice lacking RGS2 but not in RGS2 null chimeric mice receiving WT bone marrow or receiving the RGS2 gene in AM. Thus, indicating RGS2 serves as a gatekeeper of IFNg levels in AM and thereby lung's innate immune response. RGS2 functioned by forming a complex with TLR4 shielding Gaq from inducing IFNg generation and AM inflammatory signaling. Thus, inhibition of Gaq blocked IFNg generation and subverted AM transcriptome from being inflammatory to reparative type in RGS2 null mice, resolving lung injury. Highlights: RGS2 levels are inversely correlated with IFNγ in ARDS patient's AM.RGS2 in alveolar macrophages regulate the inflammatory lung injury.During pathogenic insult RGS2 functioned by forming a complex with TLR4 shielding Gαq from inducing IFNγ generation and AM inflammatory signaling. eToc Blurb: Authors demonstrate an essential role of RGS2 in macrophages in airspace to promoting anti-inflammatory function of alveolar macrophages in lung injury. The authors provided new insight into the dynamic control of innate immune response by Gαq and RGS2 axis to prevent ALI.
